Design of an Adaptive English Vocabulary Learning System Based on Fuzzy Modeling of Second Language Vocabulary Features

Second language vocabulary acquisition is dynamic, individualized, and uncertain. Traditional vocabulary learning systems often use static difficulty grading or simple frequency statistics, which are difficult to accurately adapt to learners’ cognitive states and real-time needs. In response to this issue, this article designs and implements an adaptive English vocabulary learning system based on fuzzy modeling of second language vocabulary features, using the cognitive psychology knowledge acquisition theory (three stages of understanding consolidation application) as the teaching framework. The system focuses on personalized vocabulary resource recommendation as its core goal, aiming to improve learning efficiency and stimulate learning interest. The overall architecture includes five functional modules: pre-test diagnosis, vocabulary acquisition, consolidation and application, effectiveness testing, and learning feedback, forming a closed-loop iterative learning path. At the algorithmic level, the system introduces fuzzy logic as the main inference mechanism, breaking through the rigid discrimination of “right or wrong” in classical binary logic, and using multi value membership degrees to describe the continuous spectrum of learners’ abilities. In the fuzzification stage, four behavioral indicators are selected as input variables: daily online learning duration, daily vocabulary growth, average answering time, and total vocabulary mastery. The data is obtained from real-time records in the database, and the current vocabulary mastery level and cognitive load level of learners are inferred through the fuzzy rule library. The difficulty, frequency, and presentation of recommended content are dynamically adjusted. Preliminary applications have shown that the system can adaptively optimize learning paths based on individual behavior patterns, and has significant advantages in promoting long-term vocabulary retention and enhancing autonomous learning motivation, providing a feasible technical solution for personalized second language vocabulary learning.